Current data-logging techniques generally require a framework for logging data. If a system executes multiple applications, then each of the applications can have their own framework. For example, each of the applications would create tables or files that can store data in one or more formats, and the formats can differ between applications. Moreover, different applications can use different names for logging identical or similar data. For example, one application may log a user's name whereas a different application may log the user's identifier. Applications may also use different fieldnames for data. For example, an application, “App A” in the system can log an identification (ID) of a user as “ID No.” and another application, “App B” can log the same user ID as “User ID.” Conversely, the applications can use the same names to log data that can mean different things. For example, the “App A” can log an ID of a device as “ID No.” and the “App B” can log a user ID as “ID No.”. These inconsistencies can create problems in analyzing and understanding the logged data.
Further, current data logging techniques generally lack error handling and/or data validation capabilities. They do not ensure that data being logged for a particular field is valid. For example, they may not validate that a date of birth is logged (accidentally) in place of a gender for a gender field. Furthermore, current data logging techniques sometimes require applications to create and maintain the infrastructure necessary for logging the data. This can not only give rise to potential data inconsistency problems which might evade notice, but also create a significant overhead for the system in terms of computing resources, e.g., space and processor time. If there are multiple applications in the system, each of them can consume computing resources to create and maintain the necessary infrastructure.